Category-level object pose estimation is aimed at estimating unseen objects' 6D pose within seen categories, together with their scales or canonical shape.
To the best of our knowledge, there is not currently any category-level pose estimation works focusing on transparent objects, and the works mentioned below mostly consider opaque objects. They won't work well for transparency due to their dependence on accurate depth.
Wang et al. [1]} introduced the Normalized Object Coordinate Space (NOCS) for dense 3D correspondence learning, and used the Umeyama algorithm [2]} to solve the object pose and scale.
They also contributed both a synthetic and a real dataset used extensively by the following works for benchmarking.
Later, Li et al. [3]} extended the idea towards articulated objects.
To simultaneously reconstruct the canonical point cloud and estimate the pose, Chen et al. [4]} proposed a method based on canonical shape space (CASS).
Tian et al. [5]} learned category-specific shape priors from an autoencoder, and demonstrated its power for pose estimation and shape completion.
6D-ViT [6]} and ACR-Pose [7]} extended this idea by utilizing pyramid visual transformer (PVT) and generative adversarial network (GAN) [8]} respectively. Structure-guided prior adaptation (SGPA) [9]} utilized a transformer architecture for a dynamic shape prior adaptation.
Other than learning a dense correspondence, FS-Net [10]} regressed the pose parameters directly, and it proposed to learn two orthogonal axes for 3D orientation.
Also, it contributed to an efficient data augmentation process for depth-only approaches.
GPV-Pose [11]} further improved FS-Net by adding a geometric consistency loss between 3D bounding boxes, reconstruction, and pose. Also with depth as the only input, category-level point pair feature (CPPF) [12]} could reduce the sim-to-real gap by learning deep point pairs features.
DualPoseNet [13]} benefited from rotation-invariant embedding for category-level pose estimation.
Differing from other works using segmentation networks to crop image patches as the first stage, CenterSnap [14]} presented a single-stage approach for the prediction of 3D shape, 6D pose, and size.
